Implement Infix to postfix conversion using Stack C source codes

We can implement a valid infix expression to a valid postfix expression using stacks in c as shown:

//C PROGRAM TO CONVERT GIVEN VALID INFIX EXPRESSION INTO POSTFIX EXPRESSION USING STACKS.

#include<stdio.h>
#include<math.h>
#include<string.h>
#include<stdlib.h>
#define MAX 20

char stack[MAX];
int istack[MAX];
int top = -1;
char pop();
void push(char item);

Code Generator in C

The following code generates the code itself. The code seems to be simple but it's little bit tricky and is FAQ for programmers in an interview.

#include"stdio.h"

#include"conio.h"

char *s="#include%c#include%cchar *s=%c%s%c;%cvoid main()%c{%cclrscr();-%cprintf(s,10,10,34,s,34,10,10,10,10,10,10);%cgetch();%c}";

void main(){

//clrscr();

printf(s,10,10,34,s,34,10,10,10,10,10,10);

getch();

}

Binary Tree Implementation in C with Tree Traversals

We can implement the binary tree using C so that we can add a node, delete a node, and can perform different tree traversals:

 1. Preorder tree traversal

 2. Inorder tree traversal

 3. Post order tree traversal

/// ..............................................BINARY TREE OPERATIONS AND TREE TRAVERSALS

#include<stdio.h>
#include<conio.h>
#include<malloc.h>

///.................................................................DYNAMIC STRUCTURE FOR BINARY TREE

struct tree
{
     int info;
     struct tree *left_subtree;
     struct tree *right_subtree;
};

Time & Space Complexity of Basic K-means Algorithm

The basic k-means clustering algorithm is a simple algorithm that separates the given data space into different clusters based on centroids calculation using some proximity function. Using this algorithm, we first choose the k- points as initial centroids and then each point is assigned to a cluster with the closest centroid. The algorithm is formally described as follows:

Input: A data set D containing m objects (points) with n attributes in an              Euclidean space

Output: Partitioning of m objects into k-clusters C_1, C₂, C_3, …, C_k, i.e. C_i ⊂ D and C_i ∩ C_j = ᶲ  (for 1 ≤ i, j ≤ k)

DBSCAN Algorithm Implementation in MATLAB

Density Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm locates regions of high density that are separated from one another by regions of low density. DBSCAN is a center based approach to clustering in which density is estimated for a particular point in the data set by counting the number of points within the specified radius, ɛ, of that point.

The center based approach to density allows us to classify a point as one of the three:

     Core points: These points are in the interior of the dense region
   Border points:These points are not the core points, but fall within the neighborhood of the core points
   Noise points: A noise point is a point that is neither a core point nor a border point.

        The formal definition of DBSCAN algorithm is illustrated below: